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Exploiting Dynamic Resource Allocation forEfficient Parallel Data Processing in the Cloud

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PARALLEL DATA PROCESSING IN THE CLOUDIn recent years parallel data processing has emerged to be one of the important applications for Infrastructure-as-a-Service (IaaS) clouds.Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. Examples are Googles MapReduce ,Microsofts Dryad,or Yahoo!s Map-Reduce-mergeThe most modern framework used commonly is Nepheles framework.The main goal of our project is to decrease the overloads of the main cloud and increase the performance of the cloud by using this Nepheles framework.

INTRODUCTIONToday a growing number of companies have to process huge amounts of data in a coefficient manner. Classic representatives for these companies are operators of Internet search engines, like Google, Yahoo, or Microsoft. The vast amount of data they have to deal with every day has made traditional database solutions prohibitively expensive.Cloud computing has emerged as a promising approach by using frameworks.Current data processing frameworks like Googles MapReduce or Microsofts Dryad engine have been designed for cluster environments.

EXISTING SYSTEMThe processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. The problem with these frameworks is that the resource allocation when large jobs are submitted is not efficient as they take more time for processing besides incurring more cost.disadvantages of existing systems are:Expensive Complex Increases data base organizationextra overload on cloudMore time consuming for processing.problem providing in maintenance and hard to troubleshoot.

The system as a whole is designed to overcome the major weaknesses of Map/ReduceHere we use a new framework called NepheleNephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Based on this new framework, we perform extended evaluations of MapReduce-inspired processing cloud system.We present a parallel data processor centered around a programming model of so called Parallelization Contracts (PACTs) and the scalable parallel execution engine Nephele.

PROSOSED SYSTEMThe PACT programming model is a generalization of the well-known map/reduce programming model, extending it with further second-order functions, as well as with Output Contracts that give guarantees about the behavior of a function. Our definition of PACTs allows applying several types of optimizations on the data flow during the transformation. Using this requirements in this paper we asigning the all job of the main cloud to job manager,it divide the whole task and forward them to task manager.We are adding some additional features in this project, that is We are providing a facility that a user can access other users data if he has the right to do.At the same time a message will be send to the users mobile.

Task manager process the tasks and store in the cloud. Like this we can reduce the work load and increase the performance of the cloudWe can use this concept in big cloud like Amazon or IBM as their serviceThe advantages of proposed systems are as follows:

Dynamic resource allocation Parallelism is implemented Designed to run data analysis jobs on a large amount of data Many Task Computing (MTC) has been developed Less expensive More effective More Faster Access to others data.

.LITERATURE SURVEYTitle:Map Reduce: Simplified Data Processing on Large ClustersAuthor:Jeffrey Dean and Sanjay GhemawatDescription:Map Reduce is an approach that helps us perform cluster computing. MapReduceIs aprograming model for processing large data sets wit aparallel, distributed algorithm on aclusterMapReduce has four phases: map,combine,shuttle and sort,reduce..

"Map" step:The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level treestructure. The worker node processes the smaller problem, and passes the answer back to its master node

"Reduce" step:The master node then collects the answers to all the sub-problems and combines them in some way to form the output the answer to the problem it was originally trying to solve.

Map(k1,v1)list(k2,v2)Reduce(k2, list (v2))list(v3)All values with same key are reduced together

map() functions run in parallel, creating different intermediate values from different input data setsreduce() functions also run in parallel, each working on a different output keyAll values are processed independently

Implementations:At Google: Index construction for Google Search Article clustering for Google News Statistical machine translationAt Yahoo!: Web map powering Yahoo! Search Spam detection for Yahoo! MailAt Facebook: Data mining Ad optimization

Advantages

This approach has several advantages, namely:low initial cost, and ease of maintenance (through cheap replacement of faulty machines).Fault-tolerantAutomatic parallelization & distributionProvides status and monitoring toolsClean abstraction for programmersSimple and easy to use.Flexible.Independent of the storage.Disadvantagesno high level language.reduce phase cant start until map phase is completely finished. A single fixed dataflow.Low efficiency.Restrictive semanticsPipelining Map/Reduce stages possibly inefficientMissing common DBMS utilities and featuresTransactions, updates, integrity constraints, views, Incompatible with DBMS toolsComplex values, more serialization/deserialization overhead.More complex memory management. As value maps may grow too big, the approach has potential for scalability bottleneck.Cluster computing itself can be defined as the use of large number of low end machines to form a cluster, instead of a smaller number of high end machinesalthough their purpose in the MapReduce framework is not the same as their original forms. Furthermore, the key contribution of the MapReduce framework are not the actual map and reduce functions,Provided each mapping operation is independent of the others, all maps can be performed in parallel though in practice it is limited by the number of independent data sources and/or the number of CPUs near each sourceGoogle has now outgrown it, The main reason behind it is map reduce was hindering their ability to provide near real time updates to their index. next phase of operations cant start until you finish the first. If you want to build a system that's based on series of map-reduces, there's a certain probability that something will go wrong, and this gets larger as you increase the number of operationsTitle:Dryad: Distributed Data-Parallel Programs from SequentialBuilding BlocksAuthor: M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly.

Description:Dryad is Microsoft's alternative to MapReduce. Program specification isdone by building a Direct Acyclic Graph (DAG) whose vertexes are operationsnand whose edges are data channels.An application written for Dryad is modeled as adirected acyclic graph(DAG). The DAG defines thedataflow of the application, and the vertices of the graph defines the operations that are to be performed on the data.Dryad is a general-purpose distributed execution engine forcoarse-grain data-parallel applications2 main components of Dryad is: Job manager coordinates jobs, constructs graph Name server exposes computers with network topologyDryad is what people see as map reduce done right. Dryads authors are claiming that they are able to to handle more general cases and to keep a good performance Besides allowing several inputs/outputs sets , Dryad allow runtime optimization such as aggregation . The approach offered by Dryad is pretty simple, you need to represent the flow of execution of programs in the form of a graph .Job manager schedules vertices on machines

AdvantagesMuch more general than Map ReduceGreedy algorithm is used Vertices are deterministic, and graph is acyclic, so manager can easily restartRuntime manager can reschedule vertices for better localityGraphs manually constructed, Jobs executed on vertices,Edges represent data channelsMore efficient communication,the ability to chain together multiple stages,and express more complicated computation.DryadLINQ offers a higher-level computational model where complex sequence of MapReduce steps can be easily expressed in a query language similar to SQL.More control to developer than MapReduce Choose data transport mechanism (files, TCP pipes, shared memory FIFOs)

DisadvantagesCan be used only in Cluster environments. Globally the cost of licensing both Windows servers (DryadLINQ was meant for Windows servers) and DryadLINQ compared to Unix servers and Hadoop (Free software developed by Apache) is a significantly higher. The lack of a real distributed file system. In short, in order to support large inputs, Dryad need to create a graph for virtual nodes and the communication is done via local write/distant read Graphs manually constructedDryad is not a database engine; it does not include a query planner or optimizerNo way of defining dynamic graphs.Title:Nephele/PACTs: A Programming Model and Execution Framework for Web-Scale Analytical ProcessingAuthor:Dominic Odej Kao ,Battr Stephan Volker Markl, Ewen Fabian Hueske Daniel WarnekeDescription:A parallel data processor centered around a programming model of so called Parallelization Contracts (PACTs) and the scalable parallel execution engine Nephele .

The PACT programmi